def update()

in captum/attr/_utils/summarizer.py [0:0]

• 19 lines of code
• 9 McCabe index (conditional complexity)

    def update(self, x: Union[float, Tensor, Tuple[Union[float, Tensor], ...]]):
        r"""
        Calls `update` on each `Stat` object within the summarizer

        Args:
            x (Tensor or Tuple[Tensor, ...]):
                The input(s) you wish to summarize
        """
        if self._is_inputs_tuple is None:
            self._is_inputs_tuple = isinstance(x, tuple)
        else:
            # we want input to be consistently a single input or a tuple
            assert not (self._is_inputs_tuple ^ isinstance(x, tuple))

        from captum._utils.common import _format_float_or_tensor_into_tuples

        x = _format_float_or_tensor_into_tuples(x)

        for i, inp in enumerate(x):
            if i >= len(self._summarizers):
                # _summarizers[i] is a new SummarizerSingleTensor, which
                # aims to summarize input i (i.e. x[i])
                #
                # Thus, we must copy our stats, as otherwise
                # in the best case the statistics for each input will be mangled
                # and in the worst case we will run into an error due to different
                # dimensionality in the input tensors tensors (i.e.
                # x[i].shape != x[j].shape for some pair i, j)
                stats = self._copy_stats()
                self._summarizers.append(
                    SummarizerSingleTensor(
                        stats=stats, summary_stats_indices=self._summary_stats_indicies
                    )
                )
            if not isinstance(inp, torch.Tensor):
                inp = torch.tensor(inp, dtype=torch.float)
            self._summarizers[i].update(inp)